CN106503839B - Hierarchical planning method for offshore wind farm annular current collection network - Google Patents
Hierarchical planning method for offshore wind farm annular current collection network Download PDFInfo
- Publication number
- CN106503839B CN106503839B CN201610899142.5A CN201610899142A CN106503839B CN 106503839 B CN106503839 B CN 106503839B CN 201610899142 A CN201610899142 A CN 201610899142A CN 106503839 B CN106503839 B CN 106503839B
- Authority
- CN
- China
- Prior art keywords
- partition
- node
- optimization
- cable
- clustering center
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 30
- 238000005192 partition Methods 0.000 claims abstract description 62
- 238000005457 optimization Methods 0.000 claims abstract description 55
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 23
- 239000011159 matrix material Substances 0.000 claims abstract description 19
- 230000002068 genetic effect Effects 0.000 claims abstract description 14
- 238000013461 design Methods 0.000 claims description 17
- 210000000349 chromosome Anatomy 0.000 claims description 11
- 239000013598 vector Substances 0.000 claims description 6
- 238000000638 solvent extraction Methods 0.000 claims description 4
- 238000010248 power generation Methods 0.000 description 15
- 230000000875 corresponding effect Effects 0.000 description 8
- 238000010586 diagram Methods 0.000 description 8
- 230000008901 benefit Effects 0.000 description 6
- 230000005540 biological transmission Effects 0.000 description 5
- 238000004364 calculation method Methods 0.000 description 5
- 238000012423 maintenance Methods 0.000 description 5
- 230000009466 transformation Effects 0.000 description 4
- 238000010276 construction Methods 0.000 description 3
- 238000011161 development Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 230000002596 correlated effect Effects 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 238000013178 mathematical model Methods 0.000 description 2
- 230000008439 repair process Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000003860 storage Methods 0.000 description 2
- 230000017105 transposition Effects 0.000 description 2
- 241001513371 Knautia arvensis Species 0.000 description 1
- 238000000342 Monte Carlo simulation Methods 0.000 description 1
- 239000003245 coal Substances 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 108090000623 proteins and genes Proteins 0.000 description 1
- 238000013139 quantization Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
- G06Q10/043—Optimisation of two dimensional placement, e.g. cutting of clothes or wood
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/12—Computing arrangements based on biological models using genetic models
- G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
Abstract
The invention relates to a hierarchical planning method for an offshore wind farm annular current collection network, which comprises the following steps: s1: reading in data of an offshore wind farm; s2: presetting an optimal partition number, and obtaining an initial clustering center and a partition division matrix of each partition; s3: performing preliminary string-dividing optimization on each partition; s4: updating a clustering center and a partition matrix; s5: performing annular structure string optimization on each partition by adopting a single-parent genetic algorithm; s6: and judging whether the difference value between the current optimization value and the previous optimization value is smaller than a threshold value, if not, returning to the step S4, and if so, finishing the optimization to obtain the planning result of the annular collector network. Compared with the prior art, the invention adopts a layered planning optimization mode of a transformer substation layer, a fan layer and a cable layer, simplifies the complexity of optimization, and simultaneously can reduce the cost and improve the reliability.
Description
Technical Field
The invention relates to a collecting network design technology, in particular to a layered planning method for an annular collecting network of an offshore wind farm.
Background
In recent years, environmental pollution is becoming more serious, clean energy is attracting attention, and energy safety has been raised to the national strategy. Recent world energy prospects point to the expectation that renewable energy will account for half the global increase in power generation capacity in 2035, with intermittent power supplies such as wind and solar photovoltaic accounting for 45%. With the increase of the power generation capacity of renewable energy sources, the proportion of the renewable energy sources in the global power generation structure is more than 30%, and the proportion of the renewable energy sources in the global power generation structure is more than that of coal which becomes a main energy source of the power industry by 2035 years. The offshore wind power has the great advantages that the huge wind energy resource storage amount is utilized, the offshore wind power is closer to a power grid load center, the grid connection and the utilization are convenient, and the like, and the large-scale development of the offshore wind power becomes an important task of the current wind power development in China.
Large scale long distance offshore wind farms may imply a greater number of wind turbines and longer distance power transmission requirements. The offshore wind power station is severe in offshore environment, special in conditions and higher in construction, operation and maintenance cost due to more uncertain factors than those of the offshore wind power station on land, and the power collection system is one of the parts which are most prone to faults in the operation of the large offshore wind power station. Once a fault occurs, the conditions of maintenance and overhaul work are worse and the difficulty is higher. Therefore, the offshore wind farm operation department has begun to pay high attention to the problem, and strives to scientifically evaluate and plan the reliability in the offshore wind farm planning and designing stage. Furthermore, as the size of offshore wind farms increases, the reliability requirements of the connected main network also increase gradually.
Generally, when a certain section of cable has a fault in a ring connection mode, the maximum current passing through the cable can be twice as large as that in a normal state, and the cable with larger current-carrying capacity needs to be adopted, so that the economic cost is higher. Therefore, at present, research on offshore wind farm power collection networks focuses on topological optimization of more economical and optimal radial structures, and a minimum spanning tree algorithm, a genetic algorithm and a particle swarm algorithm based on graph theory are widely applied. The annular structure is high in cost, and related research is rarely related. However, for a large offshore wind farm with large capacity and far offshore distance, due to the fact that submarine cables are difficult to locate, repair cost is high, time consumption is long, power generation benefits of the wind farm are seriously affected, and contradiction between economy and reliability caused by offshore maintenance difficulty is prominent. The installed capacity of a super-large-scale offshore wind farm in the current planning is even 2500MW, and the offshore distance of a far-sea wind farm (far offset wind farm) can reach 50-60 km. Therefore, there is a need to focus on the cost increase and reliability benefits associated with evaluating a ring structure with redundancy. In practical application, the great British London array offshore wind power station which is connected with the grid before the Olympic Games of London is 20 kilometers away from the shore, the installed capacity is 630MW for one period, and the current collection network is of an annular structure.
Banzo et al, IEEE Transactions on Power Systems, 2011,26 (3): 1338-1348(IEEE power system, volume 26, 3: 1338-1348) article of a storage Optimization model for electric power system planning of offset wind farms indicates that the overall performance of the offshore wind farm, including the power generation efficiency, the investment cost and the operation reliability, greatly depends on the design of the power system, so the design of the collection network of the offshore wind farm should greatly reduce the investment cost and ensure higher power generation efficiency and operation reliability. Shadi et al, in IEEE Transactions on Industrial Electronics, 2014, 61(1):320-328(IEEE Industrial Electronics, Vol. 61: 320-328), states that for large capacity deep offshore wind farms, the efficiency of power generation is low and the costs of construction and maintenance are high due to poor accessibility coupled with uncertainty of the offshore environment. Authors m.a. parker et al in IET Renewable Power Generation, 2013, 7 (4): an article published on 390-400(IET renewable energy power generation, volume 7 in 2013, 4: 390-400) indicates that the annular collecting network has high reliability and is suitable for large offshore wind farms. However, these documents only show the connection mode of the ring structure, and do not provide an optimization model and an optimization method for how to obtain the optimal structure.
A current collection network planning model based on an objective function is established, the current collection network is divided into a transformer substation layer, a fan layer and a cable layer according to the characteristics of a large-scale offshore current collection network, and each layer is planned in a layered mode. The method comprises the steps of firstly partitioning a substation layer by adopting a fuzzy clustering algorithm, further clustering fan layers in all current collection sub-regions by utilizing a single parent genetic algorithm, and then connecting all fans of a cable layer by combining the solution of the problem of multiple traveling salesmen. And quantitatively evaluating the economy and reliability of the topological structure to finally obtain an optimal design scheme of the offshore wind farm annular current collection network.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a method for hierarchical planning of an offshore wind farm annular current collection network.
The purpose of the invention can be realized by the following technical scheme:
a hierarchical planning method for an offshore wind farm annular collector network comprises the following steps:
s1: reading in offshore wind farm data, wherein the wind farm data comprises a fan coordinate, an offshore substation coordinate, equipment cost and parameters;
s2: presetting an optimal partition number, and obtaining an initial clustering center and a partition division matrix of each partition;
s3: performing preliminary string optimization on each partition to obtain the total cost of submarine cables on two sides of each node of all initial strings, the length of each high-voltage cable of each string and the Euclidean distance from each node to a clustering center, wherein the node is a fan;
s4: updating a clustering center and a partition matrix according to the total cost of submarine cables on two sides of each node of all sub-strings, the length of each high-voltage cable of each sub-string and the Euclidean distance from each node to the clustering center;
s5: performing annular structure string optimization on each partition by adopting a single-parent genetic algorithm, and updating the total cost of submarine cables on two sides of each node of all strings, the length of each high-voltage cable of each string and the Euclidean distance from each node to a clustering center;
s6: and calculating an optimization value according to the total cost of the submarine cables on two sides of each node of all the sub-strings, the length of each high-voltage cable of each sub-string and the Euclidean distance from each node to the clustering center in combination with the current clustering center and the partition division matrix, judging whether the difference value between the current optimization value and the previous optimization value is smaller than a threshold value, if not, returning to the step S4, and if so, finishing the optimization to obtain the planning result of the annular collector network.
The element μ in the partition dividing matrix updated in the step S4ijThe method specifically comprises the following steps:
CHij＝CHi/n
CHlj＝CHl/n
wherein: mu.sijDegree of membership, d, of node j to clustering center iijIs the Euclidean distance, λ, from node j to cluster center im、λhTo adjust the coefficients, CMijThe total cost CH of submarine cables on two sides of the node j in the partition corresponding to the clustering center iij、CHljLength equivalent to the length of the high-voltage cable, CHiIs the length of the ith section of high-voltage cable, n is the number of nodes, dljIs the Euclidean distance, CM, from node j to cluster center lljFor the total cost, CH, of the submarine cables on both sides of node j in partition llThe length of the first section of high-voltage cable, c is the number of partitions, and m is a weighting index;
the clustering center is specifically as follows:
wherein: p is a radical ofiAs the clustering center i, xjFor node j, pc is the coordinate vector of the point of common connection.
The optimization degree value Obj is:
CHij＝CHi/n
wherein: mu.sijIs the degree of membership of the node j to the clustering center i, c is the number of partitions, n is the number of nodes, dijIs the Euclidean distance, λ, from node j to cluster center im、λhTo adjust the coefficients, CMijThe total cost CH of submarine cables on two sides of the node j in the partition corresponding to the clustering center iijLength equivalent to the length of the high-voltage cable, CHiIs the length of the ith high-voltage cable.
Length CH of the ith segment of high-voltage cableiAnd taking the distance from the clustering center i to the onshore common connection point.
The step S5 specifically includes the steps of:
s51: performing annular structure string optimization on each partition by adopting a single-parent genetic algorithm;
s52: and performing cable type selection, and updating the total cost of submarine cables on two sides of each node of all sub-strings, the length of each high-voltage cable of each sub-string and the Euclidean distance from each node to a clustering center.
Compared with the prior art, the invention has the following advantages:
1) the method adopts a layered planning optimization mode of a transformer substation layer, a fan layer and a cable layer, simplifies the complexity of optimization, and can reduce the cost and improve the reliability.
2) The optimization of the clustering center and the partition matrix is obtained based on the total cost of submarine cables on two sides of each node of all the sub-strings, the length of each high-voltage cable of each sub-string and the Euclidean distance from each node to the clustering center, and the cost-oriented optimization can be realized.
3) The optimization value takes the minimum sum of the distortion of each sample point and the clustering center, namely the Euclidean distance between two vectors and the submarine cable length after the partition topology optimization as an objective function, so that the optimization effect on the submarine cable can be improved.
4) And the distance from the clustering center i to the onshore common connection point is taken, so that the calculation can be simplified while the accuracy is ensured.
Drawings
FIG. 1(a) (b) (c) is a schematic view of a current collection system topology;
FIG. 2 is a schematic flow chart of the main steps of the method of the present invention;
FIG. 3 is a schematic diagram of an example of a chromosome pair encoding mode of a single parent genetic algorithm;
FIG. 4 is a multi-traveler problem route description diagram;
FIG. 5 is a schematic diagram of an offshore wind turbine;
FIG. 6(a) is a schematic diagram of an optimized design of a collector network of a ring structure of a large offshore wind farm when the number of divisions is three;
FIG. 6(b) is a schematic diagram of an optimized design of a collector network of a ring structure of a large offshore wind farm when the number of divisions is four;
FIG. 7(a) is a schematic diagram of an optimized design of a radial structure current collection network of a large offshore wind farm when the number of divisions is three;
FIG. 7(b) is a schematic diagram of an optimized design of a radial structure current collection network of a large offshore wind farm when the number of divisions is four;
fig. 8 is a graph of the variation of available capacity of radial and ring structure current collection networks.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
Firstly, a mathematical model of the wind farm current collection network needs to be established
Factors that affect the cost of the current collection network of an offshore wind farm include: the number, the position and the voltage grade of the wind turbines, the load of electrical equipment, the topological wiring form, the cable section, the length, the arrangement of a cable trench and the like. The variables are correlated and constrained with each other, so that the current collection network optimization model has the characteristics of multiple discrete variables and strong nonlinearity. The final aim of the topological structure optimization of the AC current collection network of the large offshore wind farm is to strive for the lowest cost of the current collection network on the basis of meeting the reliability requirement. The large-scale offshore wind farm current collection network mainly comprises a submarine cable current collection network connected with each fan, an offshore substation and a transmission cable connected with a continental power grid, and has the characteristics of multiple voltage levels and multiple current collection subregions on the whole.
Fig. 1 shows a schematic diagram of a current collection system topology.
Cost of current collection network Cwhole_csThe method mainly comprises the cost of a tower bottom box transformer of the wind generating set, the cost of a medium-voltage submarine cable of a current collection network, the cost of electrical equipment (a transformer and other auxiliary electrical equipment) of an offshore substation and the cost of a high-voltage transmission cable, and is shown as the following formula:
Cwhole_cs＝CWTtran+CSUB+CCable (1)
wherein, CSUBFor offshore transformer substationTotal cost C of wind generating set and tower bottom box transformer substationWTtranThe quantization can be:
CWTtran＝Nwt·PWTtran
wherein N iswtThe number of the fans in the large-scale offshore wind farm, PWTtranIs the unit price of the box transformer substation.
Total cost of cable CCableIncluding the total cost C of medium voltage sea cableMV_cableAnd total cost of high voltage cable CHV_cableTwo parts. Since the current collection network structure is coupled with the power flow and short circuit current distribution, the nonlinear influence of the discrete variables on each other must be effectively described in the modeling process.
CCable＝CHV_cable+CMV_cable (2)
Wherein N iss、NsfAnd NsfcThe number of the offshore substations in the large offshore wind farm, the number of the feeders (namely, the number of the fan strings) connected with the s-th offshore substation, and the number of the sections of the submarine cables in the f-th feeder of the s-th substation are respectively. Ccable,sfcFor corresponding sea cable costs (including installation and transportation costs C of sea cable)install、Ctrans)。
Ccable,sfc＝Lsfc·Cunit(typei) (5)
Wherein L issfcIs the length of the sea cable at the C-th section in the f-th feeder line on the s-th substation, Cunit(typei) For the unit price (type) of the sea cablei)。Cunit(typei) The maximum allowable voltage drop Δ V is passed through the load currentmax(related to the length of each section of sea cable), maximum short circuit current, etc. L ishvcable,sIs the s th seaA length of high voltage cable between the substation and the point of common connection.
The cost of electrical equipment in offshore substations mainly includes the main transformation cost of each offshore substation and the cost of main transformation auxiliary equipment such as protection equipment and control equipment.
Wherein P isOStran,sThe unit price of the main transformer of the s-th offshore substation; n is a radical ofOStran,sThe number of main transformer stations of the s offshore substation is shown; celec,sFor the cost of other electrical equipment of the s-th offshore substation, high and medium voltage GIS switches are mainly considered herein.
Furthermore, offshore wind farms also need to take into account specific sea conditions. On one hand, the laying of the submarine cables cannot be crossed and cannot be in the same ditch, and the crossing with other pipelines in the sea area, the channel constraint and the like are required to be reduced as much as possible; on the other hand, the scheme design needs to meet the optimality and feasibility of the calculation result.
In summary, the mathematical model of the topological structure optimization design of the ring-shaped current collection network of the large offshore wind farm is described as follows:
ST:Isfc＜Irated(typei)
|ΔVsfc|＜ΔVmax
wherein, Isfc、ΔVsfc is the current flowing on the c-th section of submarine cable in the f-th feeder line of the s-th transformer substation and the voltage drop of the section of submarine cable。FiThe wind turbine nodes are a fan node set, and since submarine cables cannot be laid in a crossed mode, constraint conditions require that no intersection exists among fan clusters, and all fans are contained in the fan clusters.
The rated capacity of a large-scale offshore wind farm is generally hundreds of gigawatts or even gigawatts, under the condition, one offshore substation is difficult to meet the power transformation requirement, in addition, the number of fans of the wind farm on the scale is large, the distribution range is wide, the cost and the difficulty of laying medium-voltage submarine cables are undoubtedly increased by only arranging one offshore substation, and meanwhile, the reliability of the whole wind farm is reduced. Therefore, the division of the power transformation area of the large offshore wind farm is very necessary.
The fuzzy clustering algorithm based on the objective function is adopted for partitioning (namely partitioning) the current collecting sub-region of the wind power plant. A Fuzzy Clustering Method (FCM for short) based on an objective function is to classify clusters into a nonlinear programming problem with constraints, and obtain Fuzzy partition and cluster partition of a data set through optimization solution. The method comprises the following steps of taking coordinates of all wind generating sets in a large offshore wind farm as a data set, taking the number of offshore substations to be planned as a clustering number, and taking the distortion degree of each sample point and a clustering center, namely the minimum sum of the Euclidean distance between two vectors and the sea cable length after partition topology optimization, as an objective function to perform clustering calculation, wherein the method specifically comprises the following steps:
solving method of current collection network optimization model
Fuzzy clustering algorithm partition based on target function
St:U∈Mfc
Wherein: n is the number of nodes, namely the number of fans; c is the number of clusters (i.e., the number of Shanghai substations); mu.sijFor dividing the elements of the matrix U, representing the element j to the cluster centre iDegree of membership; dijThe Euclidean distance from node j to cluster center i is expressed as follows:
(dij)2＝|xj-pi|A＝(xj-pi)TA(xj-pi) (9)
wherein A is an identity matrix; CM (compact message processor)ijThe total cost of submarine cables on two sides of the node j in the annular group i; CH (CH)ijLength of equivalent high voltage cable: CH (CH)ij＝CHi/n，
Wherein CHiThe length of the ith section of high-voltage cable is approximately equal to the distance from the offshore substation to the onshore common connection point in the offshore subarea:
CHi＝||pi-pcc||·Cunit(typei) (10)
where pc is the coordinate vector of the point of common connection. Lambda [ alpha ]m、λhTo adjust the coefficient, a constant is used to adjust the cost data and the distance data to the same order of magnitude.
The constraint of the above objective function is the equation:
the lagrange function obtained by the lagrange condition extremum method is as follows:
l to λ and μijRespectively obtaining derivatives, and finally obtaining:
the same principle is that:
order:
then there are:
solving a partition matrix U and a clustering center piAnd finally obtaining the optimal number of the subareas, namely the planning number of the offshore transformer substation, and the clustering center in the subareas, namely the position of the offshore transformer substation in the subareas through iterative calculation, wherein the subarea to which each wind generating set belongs can be known through a partition matrix.
A method for hierarchical planning of an offshore wind farm annular collector network is disclosed, as shown in FIG. 2, and comprises the following steps:
s1: reading in offshore wind farm data, wherein the wind farm data comprises a fan coordinate, an offshore substation coordinate (if known), equipment cost and parameters;
s2: presetting an optimal partition number (which needs to be determined when the partition number is unknown), and obtaining an initial clustering center and a partition division matrix of each partition;
s3: performing preliminary string optimization on each partition to obtain the total cost of submarine cables on two sides of each node of all initial strings, the length of each high-voltage cable of each string and the Euclidean distance from each node to a clustering center, wherein the node is a fan;
s4: updating a clustering center and a partition matrix according to the total cost of submarine cables on two sides of each node of all the current sub-strings, the length of each high-voltage cable of each sub-string and the Euclidean distance from each node to the clustering center, wherein an element mu in the updated partition matrixijThe method specifically comprises the following steps:
CHij＝CHi/n
CHlj＝CHl/n
wherein: mu.sijDegree of membership, d, of node j to clustering center iijIs the Euclidean distance, λ, from node j to cluster center im、λhTo adjust the coefficients, CMijThe total cost CH of submarine cables on two sides of the node j in the partition corresponding to the clustering center iij、CHljLength equivalent to the length of the high-voltage cable, CHiIs the length of the ith high-voltage cable (i.e. the ith zone to the shore entry point), n is the number of nodes, dljIs the Euclidean distance, CM, from node j to cluster center lljFor the total cost, CH, of the submarine cables on both sides of node j in partition llThe length of the first section of high-voltage cable, c is the number of partitions, and m is a weighting index;
the clustering center is specifically as follows:
wherein: p is a radical ofiAs the clustering center i, xjFor node j, pc is the coordinate vector of the point of common connection.
Length CH of ith section of high voltage cableiAnd taking the distance from the clustering center i to the onshore common connection point.
S5: adopting a single-parent genetic algorithm to carry out annular structure string optimization on each partition, updating the total cost of submarine cables on two sides of each node of all strings, the length of each high-voltage cable of each string and the Euclidean distance from each node to a clustering center, and specifically comprising the following steps:
s51: performing annular structure string optimization on each partition by adopting a single-parent genetic algorithm; the single parent genetic algorithm is divided into strings:
after the topological structure of the large offshore wind power plant is planned and partitioned, a Single Parent Genetic Algorithm (SPGA for short) is adopted to optimize the topological structure of a certain partition. Compared with the traditional genetic algorithm, the SPGA has the outstanding characteristics that each daughter only has one parent in the generation process of the daughter population, and new individuals with different characters are generated by randomly executing transposition operators or reversal operators on the parents. The single-parent transposition operator can ensure that a new generation of individuals has the basic characteristic of becoming a feasible solution and can also improve the searching capacity of a solution space. The method has the advantages that the execution speed of the single-parent reverse operator is high, the effective gene segments in the parent can be directly inherited into the daughter, one chromosome carries path information (namely, a path chromosome) and the other chromosome carries grouping information (namely, a breakpoint chromosome) by adopting a parallel inheritance mode of a chromosome pair, and the two chromosomes adopt simple and intuitive integer codes, as shown in figure 3.
One chromosome pair represents a topology of each subset of the electrical networks in the partition, i.e., the multi-traveler path that characterizes the submarine cable path, so that the number of offshore wind turbines connected to each section of submarine cable and the maximum short-circuit current flowing through can be obtained. And (4) carrying out submarine cable type selection and verification according to the current-carrying capacity and the maximum short-circuit current of each section of submarine cable. And obtaining the manufacturing cost of the medium-voltage submarine cable according to the selected type of the submarine cable, the corresponding unit price of the submarine cable and the length of the submarine cable. The cost of the medium-voltage submarine cable is added with the cost of other equipment in the subset electric network, such as a box transformer substation, an offshore substation and the like, so that the cost of the subset electric network topology mode corresponding to the chromosome is obtained and is used as the fitness value of the chromosome, and the specific calculation method is analyzed in the optimization model.
Considering the special constraint that submarine cable connection can not be crossed, a penalty function positively correlated with the crossing times is added into the fitness function. By setting corresponding parameter values, the situation of submarine cable crossing does not occur in the final optimization scheme.
S52: and performing cable type selection, and updating the total cost of submarine cables on two sides of each node of all sub-strings, the length of each high-voltage cable of each sub-string and the Euclidean distance from each node to a clustering center. And simultaneously, selecting the type of the cable, calculating the total cost of the medium-voltage cable, and adding the total cost of the medium-voltage cable, the box transformer substation cost and the offshore substation cost to obtain the total cost of the subarea current collection system, namely the individual adaptability value.
Routing connection of multi-traveler problem: the multi-traveler Problem (MTSP) is an extension of the traveler Problem, that is, there are N cities, and M travelers respectively start from the same city (or different cities), and walk a travel route to visit all cities, so that each city has only one traveler passing through (except for the starting city), and finally each traveler returns to the original starting city (forming a circular path), and the total travel distance is shortest. By taking the solution of the problems of multiple traveling salesmen as a reference, an offshore wind farm partition containing N wind generating sets is regarded as a set of N cities, and the wind generating sets in the partition are divided into M annular wind generating set strings. Therefore, the design problem of the ring current collection network topology of the offshore wind farm is converted into the MTSP problem that M travelers visit N cities. In addition, according to the number of cities walked by each traveling salesman, whether the tasks of the MTSP are equally divided is judged, namely whether the number of the connected fans in each string is necessarily equal or close. The route description for the multi-traveler problem is shown in fig. 4.
S6: and calculating an optimization value according to the total cost of the submarine cables on two sides of each node of all the sub-strings, the length of each high-voltage cable of each sub-string and the Euclidean distance from each node to the clustering center in combination with the current clustering center and the partition division matrix, judging whether the difference value between the current optimization value and the previous optimization value is smaller than a threshold value, if not, returning to the step S4, and if so, finishing the optimization to obtain the planning result of the annular collector network.
The optimization value Obj is:
CHij＝CHi/n
wherein: mu.sijIs the degree of membership of the node j to the clustering center i, c is the number of partitions, n is the number of nodes, dijIs the Euclidean distance, λ, from node j to cluster center im、λhTo adjust the coefficients, CMijThe total cost CH of submarine cables on two sides of the node j in the partition corresponding to the clustering center iijLength equivalent to the length of the high-voltage cable, CHiIs the length of the ith high-voltage cable.
Examples of specific applications
The case wind power plant is a large offshore wind power plant which is 50km offshore and comprises 259 wind power generating sets with single machine capacity of 3.5MW, and specific parameters of the offshore wind power generating sets are shown in a table 1. Coordinates of the offshore wind generating set are determined, and the wind power station is arranged as shown in the figure 5. By integrating the factors of equipment cost and transmission loss, it is generally considered that 30-36 kV is the optimal voltage level for connection between fans in an alternating current electrical system. The power collection network is connected by submarine medium-voltage cables, the voltage level is 35kV, the power transmission cables are high-voltage submarine cables, and the voltage level is 110kV or 220 kV.
TABLE 1 Individual offshore wind turbine parameters
On the basis of considering factors such as the number of feasible subareas of a large offshore wind power plant, the number of clusters of offshore wind generating sets which are in feasible annular connection in the subareas, the type selection of collecting network equipment and the like, the optimal design method for the collecting network with the annular structure provided by the invention is adopted to optimally design the collecting network of the actual offshore wind power plant in a plan. The main parameters of the 35kV submarine cable are shown in table 2, according to the current-carrying capacity and short-circuit characteristics of the submarine cable, the submarine cable with a 400 section can allow 9 fans of 3.5MW to flow simultaneously, if the number exceeds this, no thicker submarine cable is matched with the submarine cable, and the submarine cable cannot be selected. Therefore, the number of fans per cluster is optimized between 1 and 9. The fewer the number of the fans in each cluster, the higher the reliability, but the huge economic cost; the more the number of the fans in each series is, the higher the economical efficiency is, and certain reliability requirements need to be met.
TABLE 2 Main parameters of Medium Voltage (35kV) submarine Cable
As can be seen from table 2, the medium voltage submarine cable is expensive, the laying length of the medium voltage submarine cable inside the 100MW offshore wind farm of the east sea bridge in the upper sea is about 70km, and if the capacity of the offshore wind farm is larger, the medium voltage submarine cable has a larger optimized space. The method takes the economic optimization as a target, takes the reliability as a constraint, divides a large-scale offshore current collection system into a transformer substation layer, a fan layer and a submarine cable layer according to the characteristics of the large-scale offshore current collection system under the condition of meeting the requirements of special offshore conditions, and adopts the algorithm to respectively optimize and solve each layer. The number of the subareas is optimized at a substation level (the number of the fans in each subarea is limited by the current-carrying capacity and the short-circuit characteristic of a high-voltage submarine cable), the number of the subareas is optimized at a fan level (the number of the fans in each subarea is limited by the current-carrying capacity and the short-circuit characteristic of a medium-voltage submarine cable), and the cross section and the length of the submarine cable are optimized at a submarine cable layer. Two optimization design schemes with the partition numbers of 3 and 4 are finally obtained, specifically shown in fig. 6, and the summary results of the costs of the electrical equipment in the optimization schemes are given, and are shown in tables 2 and 3.
TABLE 2 summary of substation equipment usage and cost
TABLE 3 summary of medium voltage submarine cables of different cross-sections
For comparison, the case wind farm is also designed with a radial topology. The radial structure topology adopts the minimum spanning tree algorithm based on graph theory which is widely used at present, and the design result under the same partition number is shown in FIG. 7. The two types of typical connection structures are used for quantitatively evaluating the optimization results in both economic and reliability aspects. The total cost of the current collection network for both configurations is shown in table 4.
TABLE 4 Total cost of the current-collecting network under different optimization algorithms
According to the existing power generation reliability evaluation experience, under the conditions of lacking power generation system reliability data, load prediction data and insufficient experience, a probability evaluation method is usually selected to be ideal. The offshore wind farm power collection system has numerous and various types of equipment, and complex state combination and operation conditions, and the reliability of the power collection system is estimated by adopting a Monte Carlo simulation method in consideration of the availability [19] of the offshore wind power generation set. The selection of the wind generating sets, the cable failure rate and the repair time are shown in table 5, and the time chart of the number of the available wind generating sets in the collection network with the radial and annular structure with the optimal economy in one year is shown in fig. 8.
TABLE 5 Fault parameters of offshore wind turbines and cables
The available fan capacity for a power collection network of both configurations over the year is shown in fig. 8. From an economic point of view, the cost of the annular collector network is increased by 24 percent compared with that of the radial structure collector network, but the average available capacity of the annular collector network is also increased by 23.7 percent. The cost difference between the two is mainly in the cost of the cable. The reliability of the annular structure wind power plant is obviously superior to that of the radial structure wind power plant, when the offshore wind power plant adopts the annular structure collecting network, the available capacity of the offshore wind power plant is basically equal to the rated total capacity, the stable and efficient electric energy output of the wind power plant is ensured, and when the offshore wind power plant adopts the radial structure collecting network, the available capacity of the offshore wind power plant has large variation amplitude and lower average capacity. Although the economic cost of the ring structure is larger than that of the radial structure, the improvement of the reliability is obvious, so that the ring structure is not lost as an advantageous choice for the wind power plant with strict reliability requirements.
The emphasis here is on comparing the merits of different topologies, the average available capacity in fig. 8 does not account for the wind energy effect, and the generated energy yield value accounting for the wind energy effect is shown in table 6. Annual hours of use are affected by offshore wind energy, but wind energy affects the same for the same offshore wind farm, so the reference 2600 hours of the offshore wind farm design of the above-sea east-sea bridge is used herein as a reference standard. The power generation output value is calculated according to the condition that the power price of the offshore wind power project which is put into operation before 2017 and is determined by the Chinese national development and reform Commission is 0.85 yuan/kWh. A comprehensive comparison of the different topologies is shown in table 6.
TABLE 6 comprehensive comparison of different topologies
From table 6, it can be known that although the investment cost of the annular structure is higher in the initial construction period, the power generation output value of the wind power plant in the 20-year operation period is far higher than that of the radial structure due to the higher reliability of the annular structure. The data in table 6 only consider the reliability factor of the topology structure, but do not consider the problem of difficult maintenance of the offshore wind farm due to poor accessibility, and if the accessibility influence of the offshore wind farm is considered, the benefits brought by the ring structure are more huge.
Claims (2)
1. A method for hierarchical planning of an offshore wind farm annular current collection network is characterized in that,
establishing a current collection network planning model based on an objective function, dividing the current collection network into a transformer substation layer, a fan layer and a cable layer according to the characteristics of a large-scale offshore current collection network, and performing layered planning on each layer; firstly, partitioning a substation layer by adopting a fuzzy clustering algorithm, further clustering fan layers in all current collecting sub-regions by utilizing a single parent genetic algorithm, and connecting all fans of a cable layer by combining a solution idea of a multi-traveler problem; quantitatively evaluating the economy and reliability of the topological structure to finally obtain an optimal design scheme of the offshore wind farm annular current collection network;
the method comprises the following steps:
s1: reading in offshore wind farm data, wherein the wind farm data comprises a fan coordinate, an offshore substation coordinate, equipment cost and parameters;
s2: presetting an optimal partition number, and obtaining an initial clustering center and a partition division matrix of each partition;
s3: performing preliminary string optimization on each partition to obtain the total cost of submarine cables on two sides of each node of all initial strings, the length of each high-voltage cable of each string and the Euclidean distance from each node to a clustering center, wherein the node is a fan;
s4: updating a clustering center and a partition matrix according to the total cost of submarine cables on two sides of each node of all sub-strings, the length of each high-voltage cable of each sub-string and the Euclidean distance from each node to the clustering center;
s5: performing annular structure string optimization on each partition by adopting a single-parent genetic algorithm, and updating the total cost of submarine cables on two sides of each node of all strings, the length of each high-voltage cable of each string and the Euclidean distance from each node to a clustering center;
s6: calculating an optimization value according to the total cost of submarine cables on two sides of each node of all sub-strings, the length of each high-voltage cable of each sub-string and the Euclidean distance from each node to the clustering center in combination with the current clustering center and the partition division matrix, judging whether the difference value between the current optimization value and the previous optimization value is smaller than a threshold value, if not, returning to the step S4, if so, finishing the optimization, and obtaining a planning result of the annular collector network;
the element μ in the partition dividing matrix updated in the step S4ijThe method specifically comprises the following steps:
CHij＝CHi/n
CHlj＝CHl/n
wherein: mu.sijDegree of membership, d, of node j to clustering center iijIs the Euclidean distance, λ, of node j to cluster center im、λhTo adjust the coefficients, CMijThe total cost CH of submarine cables on two sides of the node j in the partition corresponding to the clustering center iij、CHljFor a length equivalent to a high-voltage cable, CHiIs the length of the ith section of high-voltage cable, n is the number of nodes, dljEuclidean distance, CM, of node j to cluster center lljFor the total cost, CH, of the submarine cables on both sides of node j in partition llThe length of the first section of high-voltage cable, c is the number of partitions, and m is a weighting index;
the clustering center is specifically as follows:
wherein: piAs the clustering center i, xjIs a node j, and pc is a coordinate vector of the common connection point;
the optimization degree value Obj is
CHij＝CHi/n
The step S5 specifically includes the steps of:
s51: performing ring structure string optimization on each partition by adopting a single parent genetic algorithm, wherein one chromosome pair represents a topological structure of each subset electric network in the partition, namely a multi-traveling-quotient path representing a submarine cable path, so that the number of the offshore wind turbines connected to each section of submarine cable and the maximum short-circuit current flowing through each section of submarine cable are obtained;
s52: and performing cable type selection, and updating the total cost of submarine cables on two sides of each node of all sub-strings, the length of each high-voltage cable of each sub-string and the Euclidean distance from each node to a clustering center.
2. The method for hierarchical planning of the offshore wind farm annular collector network according to claim 1, wherein the length CH of the ith high-voltage cableiAnd taking the distance from the clustering center i to the onshore common connection point.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610899142.5A CN106503839B (en) | 2016-10-14 | 2016-10-14 | Hierarchical planning method for offshore wind farm annular current collection network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610899142.5A CN106503839B (en) | 2016-10-14 | 2016-10-14 | Hierarchical planning method for offshore wind farm annular current collection network |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106503839A CN106503839A (en) | 2017-03-15 |
| CN106503839B true CN106503839B (en) | 2021-09-14 |
Family
ID=58295292
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610899142.5A Active CN106503839B (en) | 2016-10-14 | 2016-10-14 | Hierarchical planning method for offshore wind farm annular current collection network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106503839B (en) |
Families Citing this family (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106991229B (en) * | 2017-03-30 | 2020-08-21 | 华北电力大学 | Wind power plant equivalent modeling method for complex topology |
| CN110137951B (en) * | 2019-05-16 | 2021-06-08 | 广东电力交易中心有限责任公司 | Market partitioning method and device based on node electricity price |
| CN110334391B (en) * | 2019-05-23 | 2023-03-28 | 明阳智慧能源集团股份公司 | Automatic planning method for collecting circuit of multi-dimensional constraint wind power plant |
| CN110416925B (en) * | 2019-07-12 | 2021-07-20 | 上海电机学院 | Submarine cable wiring design method for offshore wind power |
| CN110705813B (en) * | 2019-07-23 | 2022-11-22 | 电子科技大学 | Hybrid cable connection method considering reliability of wind power plant current collection system |
| CN111079982B (en) * | 2019-11-25 | 2023-06-09 | 上海电气风电集团股份有限公司 | Planning method, system, medium and electronic equipment for cable path of wind power plant |
| CN110704995B (en) * | 2019-11-28 | 2020-05-01 | 电子科技大学中山学院 | Cable layout method and computer storage medium for multiple types of fans of multi-substation |
| CN111030179B (en) * | 2019-12-26 | 2023-08-25 | 上海电气风电集团股份有限公司 | Optimization method, optimization system and computer readable storage medium for wind farm layout |
| CN111724273B (en) * | 2020-05-09 | 2023-04-14 | 中国大唐集团科学技术研究院有限公司火力发电技术研究院 | Method for automatically planning current collection circuit of offshore wind farm by adopting large-capacity wind turbine generator |
| CN112070115B (en) * | 2020-08-03 | 2024-02-02 | 国家电网有限公司 | Power grid construction method based on traveling salesman problem algorithm and tide flow analysis |
| CN112436439B (en) * | 2020-11-11 | 2022-11-18 | 阳光新能源开发股份有限公司 | Method and device for laying cable bridge of photovoltaic power station |
| CN112487659B (en) * | 2020-12-15 | 2022-08-02 | 国网江苏省电力有限公司经济技术研究院 | Optimal design method and system for offshore wind farm current collection system |
| CN112529735B (en) * | 2020-12-23 | 2022-08-26 | 南方电网科学研究院有限责任公司 | Equivalent clustering method and device for wind turbine generators of wind power plant and storage medium |
| CN113285666B (en) * | 2021-06-08 | 2023-11-07 | 安徽华电工程咨询设计有限公司 | Layout design method for photovoltaic cable trough box |
| CN113328471A (en) * | 2021-06-11 | 2021-08-31 | 中国华能集团清洁能源技术研究院有限公司 | Wind field arrangement method and system suitable for high-capacity multi-site offshore wind power output |
| CN114186488A (en) * | 2021-12-06 | 2022-03-15 | 国网福建省电力有限公司经济技术研究院 | Topological structure optimization method and system for large-scale offshore wind farm current collection system |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2006133021A2 (en) * | 2005-06-03 | 2006-12-14 | Aspeed Software Corporation | Method and system for conditioning of numerical algorithms for solving optimization problems within a genetic framework |
| CN104680262A (en) * | 2015-03-18 | 2015-06-03 | 国网上海市电力公司 | Receiving-end grid optimal layering and districting scheme obtaining method |
| CN105184683A (en) * | 2015-10-10 | 2015-12-23 | 华北电力科学研究院有限责任公司 | Probability clustering method based on wind electric field operation data |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103793566B (en) * | 2014-01-28 | 2016-11-02 | 同济大学 | A kind of wind energy turbine set polytypic blower fan based on genetic algorithm optimizes arrangement method |
| CN105512472B (en) * | 2015-11-30 | 2018-05-01 | 国网青海省电力公司 | Large-scale wind electricity base power collects system topology hierarchy optimization design method |
-
2016
- 2016-10-14 CN CN201610899142.5A patent/CN106503839B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2006133021A2 (en) * | 2005-06-03 | 2006-12-14 | Aspeed Software Corporation | Method and system for conditioning of numerical algorithms for solving optimization problems within a genetic framework |
| CN104680262A (en) * | 2015-03-18 | 2015-06-03 | 国网上海市电力公司 | Receiving-end grid optimal layering and districting scheme obtaining method |
| CN105184683A (en) * | 2015-10-10 | 2015-12-23 | 华北电力科学研究院有限责任公司 | Probability clustering method based on wind electric field operation data |
Non-Patent Citations (3)
| Title |
|---|
| 云南电网网架重构策略研究;张雪丽，;《中国优秀硕士学位论文全文数据库工程科技II辑（月刊）》;20150315(第03期);C042-1023 * |
| 基于免疫单亲遗传和模糊C均值的聚类算法;蒋红芬,等；;《江苏技术师范学院学报》;20060831;第12卷(第4期);46-50 * |
| 基于单亲遗传算法和模糊C-均值算法的混合聚类算法;韩斌，;《华东船舶工业学院学报》;20000630;第15卷(第3期);20-23 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106503839A (en) | 2017-03-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106503839B (en) | Hierarchical planning method for offshore wind farm annular current collection network | |
| Hou et al. | Optimisation of offshore wind farm cable connection layout considering levelised production cost using dynamic minimum spanning tree algorithm | |
| Gonzalez-Longatt et al. | Optimal electric network design for a large offshore wind farm based on a modified genetic algorithm approach | |
| CN112487659B (en) | Optimal design method and system for offshore wind farm current collection system | |
| CN109638870B (en) | Phase modulator configuration method of extra-high voltage direct current transmission end power grid | |
| Srikakulapu et al. | Optimized design of collector topology for offshore wind farm based on ant colony optimization with multiple travelling salesman problem | |
| CN108304972B (en) | Active power distribution network frame planning method based on supply and demand interaction and DG (distributed generation) operation characteristics | |
| CN109598377B (en) | AC/DC hybrid power distribution network robust planning method based on fault constraint | |
| CN112398678B (en) | Operation mode topological structure feature extraction method suitable for multi-source heterogeneous power grid | |
| Fu et al. | Collection system topology for deep-sea offshore wind farms considering wind characteristics | |
| CN109740975A (en) | Consider the tidal current energy generating field planing method of Tide resource assessment and flow velocity direction | |
| Lingling et al. | Optimization of electrical connection scheme for large offshore wind farm with genetic algorithm | |
| Gonzalez-Longatt | Optimal offshore wind farms' collector design based on the multiple travelling salesman problem and genetic algorithm | |
| CN109002938B (en) | Double-layer planning method for alternating current-direct current hybrid power distribution network considering N-1 safety criterion | |
| Shen et al. | Optimal planning for electrical collector system of offshore wind farm with double-sided ring topology | |
| Ren et al. | An alterable weight minimum spanning tree method for electrical collector system planning in tidal current generation farms | |
| CN115496427B (en) | Flexible interconnection investment planning decision method for multi-microgrid system | |
| Hou et al. | Offshore wind farm cable connection configuration optimization using dynamic minimum spanning tree algorithm | |
| Paleba et al. | Optimal placement and sizing distributed wind generation using particle swarm optimization in distribution system | |
| Li et al. | Cable connection optimization for onshore wind farms considering restricted area and topography | |
| CN115495989A (en) | Offshore wind farm current collection system topological structure design method considering wind characteristics | |
| Li et al. | Optimization of the offshore wind power grid-connected structure based on an improved genetic algorithm | |
| Jiang et al. | Expansion planning method of offshore multiplatform power system with wind power considering cable size selection | |
| Chen et al. | Grouping-Based Optimal Design of Collector System Topology for a Large-Scale Offshore Wind Farm by Improved Simulated Annealing | |
| Dong et al. | Planning for Network Expansion Based on Prim Algorithm and Reinforcement Learning |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CP03 | Change of name, title or address |
Address after: 201306 No. 1851, Hucheng Ring Road, Pudong New Area, Shanghai Patentee after: Shanghai Electric Power University Country or region after: China Address before: 200090 No. 2103, Pingliang Road, Shanghai, Yangpu District Patentee before: SHANGHAI University OF ELECTRIC POWER Country or region before: China |
|
| CP03 | Change of name, title or address | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20240222 Address after: 200120, Room 1006A, No. 958 Lujiazui Ring Road, Pudong New Area, Shanghai Patentee after: Shanghai Shenergy New Energy Investment Co.,Ltd. Country or region after: China Address before: 201306 No. 1851, Hucheng Ring Road, Pudong New Area, Shanghai Patentee before: Shanghai Electric Power University Country or region before: China |
|
| TR01 | Transfer of patent right |